Analysis was completed in R v3.4.3 [5] using the following packages.
library(tidyverse)
library(phyloseq)
library(ggplot2)
library(dplyr)
library(stringr)
library(magrittr)
library(knitr)
library(gridExtra)
library(grid)
library(randomcoloR)
Generate a random colour palette for use in figures.
palette <- distinctColorPalette(40)
Data were loaded into R and samples normalized to 100,000 sequences per sample. Reproducibility was kept by setting a random seed.
load("mothur_phyloseq.RData")
load("qiime2_phyloseq.RData")
set.seed(4831)
m.norm = rarefy_even_depth(mothur, sample.size=100000)
q.norm = rarefy_even_depth(qiime2, sample.size=100000)
Relative abundance percentages were calculated for the data.
m.percent = transform_sample_counts(m.norm, function(x) 100 * x/sum(x))
q.percent = transform_sample_counts(q.norm, function(x) 100 * x/sum(x))
Chloroflexi phylum was chosen.
phylum_name_mothur = "Chloroflexi"
phylum_name_qiime2 = "D_1__Chloroflexi"
Shannon’s diversity index and Chao1 were calculated for the total microbial community across depth and oxygen concentration gradients for both mothur and QIIME2.
# Alpha-diversity of total community for mothur
m.alpha = estimate_richness(m.norm, measures = c("Chao1", "Shannon"))
m.meta.alpha = full_join(rownames_to_column(m.alpha),
rownames_to_column(data.frame(m.percent@sam_data)), by = "rowname")
m.shannon.depth.plot <- m.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Shannon)) +
geom_smooth(method='auto', aes(x=as.numeric(Depth_m), y=Shannon)) +
labs(title="Mothur", y="Shannon's diversity index", x=NULL)
m.chao1.depth.plot <- m.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Chao1)) +
geom_smooth(method='auto', aes(x=as.numeric(Depth_m), y=Chao1)) +
labs(title="Mothur", y="Chao1 richness estimator", x="Depth (m)")
m.shannon.o2.plot <- m.meta.alpha %>%
ggplot() +
geom_point(aes(x=O2_uM, y=Shannon)) +
# geom_smooth(method='auto', aes(x=as.numeric(O2_uM), y=Shannon)) +
labs(title="Mothur", y="Shannon's diversity index", x=NULL)
m.chao1.o2.plot <- m.meta.alpha %>%
ggplot() +
geom_point(aes(x=O2_uM, y=Chao1)) +
# geom_smooth(method='auto', aes(x=as.numeric(O2_uM), y=Chao1)) +
labs(title="Mothur", y="Chao1 richness estimator", x="Oxygen (uM)")
# Alpha-diversity of total community for qiime2
q.alpha = estimate_richness(q.norm, measures = c("Chao1", "Shannon"))
q.meta.alpha = full_join(rownames_to_column(q.alpha),
rownames_to_column(data.frame(q.percent@sam_data)), by = "rowname")
q.shannon.depth.plot <- q.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Shannon)) +
geom_smooth(method='auto', aes(x=as.numeric(Depth_m), y=Shannon)) +
labs(title="Qiime2", y=NULL, x=NULL)
q.chao1.depth.plot <- q.meta.alpha %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Chao1)) +
geom_smooth(method='auto', aes(x=as.numeric(Depth_m), y=Chao1)) +
labs(title="Qiime2", y=NULL, x="Depth (m)")
q.shannon.o2.plot <- q.meta.alpha %>%
ggplot() +
geom_point(aes(x=O2_uM, y=Shannon)) +
# geom_smooth(method='auto', aes(x=as.numeric(O2_uM), y=Shannon)) +
labs(title="Qiime2", y=NULL, x=NULL)
q.chao1.o2.plot <- q.meta.alpha %>%
ggplot() +
geom_point(aes(x=O2_uM, y=Chao1)) +
# geom_smooth(method='auto', aes(x=as.numeric(O2_uM), y=Chao1)) +
labs(title="Qiime2", y=NULL, x="Oxygen (uM)")
# Plotting depth graph
grid.arrange(m.shannon.depth.plot, q.shannon.depth.plot, m.chao1.depth.plot, q.chao1.depth.plot, ncol=2, top=textGrob("Figure 1 Alpha-diversity across Depth",gp=gpar(fontsize=16,font=3)))
The same patterns of alpha-diversity (Shannon’s diversity index and the Chao1 richness estimator) can be observed across depth for both mothur and QIIME2 (Fig. 1). There is a slightly lower diversity in surface waters (0m) compared to 100m depth. Peak diversity is reached at ~100-120m and diversity decreases with greater depth, with a slight increase at 200m for all but Shannon’s diversity index for QIIME2.
Note, however, that despite the similarity in the alpha-diversity pattern, the comparison of mothur versus QIIME2 shows difference: across all depths, mothur OTU analysis resulted in a lower alpha-diversity than the QIIME2 ASV analysis when measured with the Shannon’s diversity index and a higher alpha-diversity than the QIIME2 ASV analysis when measured with Chao1.
# Plotting oxygen graph
grid.arrange(m.shannon.o2.plot, q.shannon.o2.plot, m.chao1.o2.plot, q.chao1.o2.plot, ncol=2, top=textGrob("Figure 2 Alpha-diversity across Oxygen Concentration",gp=gpar(fontsize=16,font=3)))
Looking at Shannon’s diversity across oxygen concentration (Fig. 2), we find that at equivalent depths QIIME2 has a greater diversity than mothur. However, the pattern exhibited by both mothur and QIIME2 data is still similar. The three lowest diversity points (note for mothur: 2 points at 2.35 overlap) are at an oxygen concentration of 0 uM, while the highest diversity is found at an oxygen concentration of ~38 uM. The band of 95% confidence intervals was not plotted due to the lack of data between ~38 uM and ~217 uM of oxygen.
Comparing Chao1 at different oxygen levels for mothur and QIIME2 shows that the patterns differ. While the three lowest diversity points are still at 0 uM of oxygen, for mothur the highest diversity in terms of Chao1 is at an oxygen concentration of ~38 uM, while for QIIME2 it is at an oxygen concentration of ~32uM. For both, oxygen concentration of ~217 uM shows a notable decrease in diversity compared to lower, non-zero oxygen concentrations. Chao1 exhibited a greater drop at ~217 uM of oxygen compared to Shannon.
# Mothur
m.phyla.plot = m.percent %>%
plot_bar(fill="Phylum")+
geom_bar(aes(fill=Phylum), stat="identity")+
labs(title="Figure 3 Phyla across Samples for Mothur", y="Abundance (%)")+
scale_fill_manual(values=palette)
# QIIME2
q.phyla.plot = q.percent %>%
plot_bar(fill="Phylum")+
geom_bar(aes(fill=Phylum), stat="identity")+
labs(title="Figure 4 Phyla across Samples for QIIME2", y="Abundance (%)")+
scale_fill_manual(values=palette)
28 and 29 taxons were identified at the phylum level with Mothur and QIIME2, respectively (Fig. 3,4). Out of these identified phyla in both mothur and QIIME2, ~4 dominated the community composition in terms of abundance: Proteobacteria, Bacteroidetes, Thaumarchaeota and Actinobacteria (from most to less abundant). Other phyla that are noticeably more abundant include Cyanobacteria, Deferribacteres, Euryarchaeota, Firmiucutes, Gemmatimonadetes, Marinimicrobia, Nitrospinae, Planctomycetes and Verrucomicrobia. Our phylum of interest, Chloroflexi, makes up from 0 to 6% of the microbial community in the collected samples depending on depth. A more specific naming system seems to be used by mothur than QIIME2, which results in a more descriptive labelling of the population composition in the former.
# Significance across depth
m.chlor.lm = m.norm %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ Depth_m, .) %>%
summary()
q.chlor.lm = q.norm %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ Depth_m, .) %>%
summary()
taxon.abundance = data.frame("Estimate" = numeric(0), "Std. Error"= numeric(0),"t value"= numeric(0),"Pr(>|t|)"= numeric(0))
taxon.abundance <- rbind(taxon.abundance, m.chlor.lm$coefficients["Depth_m",])
taxon.abundance <- rbind(taxon.abundance, q.chlor.lm$coefficients["Depth_m",])
rownames(taxon.abundance) <- (c("mothur", "qiime2"))
colnames(taxon.abundance) <- (c("Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
kable(taxon.abundance,caption="Table 1 Correlation Data of Chloroflexi Phylum across Depth")
| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| mothur | 1.327529 | 0.6389862 | 2.077554 | 0.0923485 |
| qiime2 | 2.622128 | 0.4212043 | 6.225311 | 0.0015644 |
m.abd.depth.plot <- m.percent %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), Depth_m=mean(Depth_m)) %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(Depth_m), y=Abundance_sum)) +
labs(title="Mothur", y="Abundance (%)", x="Depth (m)")
q.abd.depth.plot <- q.percent %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), Depth_m=mean(Depth_m)) %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(Depth_m), y=Abundance_sum)) +
labs(title="QIIME2", y=NULL, x="Depth (m)")
# Significance across oxygen concentrations
m.chlor.lm.ox = m.norm %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ O2_uM, .) %>%
summary()
q.chlor.lm.ox = q.norm %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
tax_glom(taxrank = 'Phylum') %>%
psmelt() %>%
lm(Abundance ~ O2_uM, .) %>%
summary()
taxon.abundance.ox = data.frame("Estimate" = numeric(0), "Std. Error"= numeric(0),"t value"= numeric(0),"Pr(>|t|)"= numeric(0))
taxon.abundance.ox <- rbind(taxon.abundance.ox, m.chlor.lm.ox$coefficients["O2_uM",])
taxon.abundance.ox <- rbind(taxon.abundance.ox, q.chlor.lm.ox$coefficients["O2_uM",])
rownames(taxon.abundance.ox) <- (c("mothur", "qiime2"))
colnames(taxon.abundance.ox) <- (c("Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
kable(taxon.abundance.ox,caption="Table 2 Correlation Data of Chloroflexi Phylum across Oxygen Concentration")
| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| mothur | -0.750471 | 0.5865861 | -1.279387 | 0.2569128 |
| qiime2 | -1.731996 | 0.5762708 | -3.005525 | 0.0299088 |
m.abd.o2.plot <- m.percent %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), O2_uM=mean(O2_uM)) %>%
ggplot() +
geom_point(aes(x=O2_uM, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(O2_uM), y=Abundance_sum)) +
labs(title="Mothur", y="Abundance (%)", x="O2 (uM)")
q.abd.o2.plot <- q.percent %>%
subset_taxa(Phylum==phylum_name_qiime2) %>%
psmelt() %>%
group_by(Sample) %>%
summarize(Abundance_sum=sum(Abundance), O2_uM=mean(O2_uM)) %>%
ggplot() +
geom_point(aes(x=O2_uM, y=Abundance_sum)) +
geom_smooth(method='lm', aes(x=as.numeric(O2_uM), y=Abundance_sum)) +
labs(title="QIIME2", y=NULL, x="O2 (uM)")
# Plotting depth graph
grid.arrange(m.abd.depth.plot, q.abd.depth.plot, ncol=2, top=textGrob("Figure 5 Chloroflexi Abundance across Depth",gp=gpar(fontsize=16,font=3)))
# Plotting oxygen graph
grid.arrange(m.abd.o2.plot, q.abd.o2.plot, ncol=2, top=textGrob("Figure 6 Chloroflexi Abundance across Oxygen Concentration",gp=gpar(fontsize=16,font=3)))
Linear regression analysis of Chloroflexi relative abundance across depth revealed variations between Mothur’s OTU and QIIME2’s ASV clustering (Figure 5). ASV clusters revealed a significant correlation (p<0.05), while OTU clusters did not. Both correlations were found to be positive.
Similarly, linear regression analysis across oxygen concentration (Figure 6) revealed significant trends in terms of OTU clusters (p<0.05), but not ASV clusters. Both correlations were found to be negative.
# Number of OTUs
m.tax_table = data.frame(m.norm@tax_table)
m.filtered = m.tax_table %>%
rownames_to_column('OTU') %>%
filter(Phylum==phylum_name_mothur) %>%
column_to_rownames('OTU')
m.rownumber = nrow(m.filtered)
# Number of ASVs
q.tax_table = data.frame(q.norm@tax_table)
q.filtered = q.tax_table %>%
rownames_to_column('ASV') %>%
filter(Phylum==phylum_name_qiime2) %>%
column_to_rownames('ASV')
q.rownumber = nrow(q.filtered)
For Chloroflexi, the number of OTUs was found to be 34, and the number of ASVs was found to be 47.
# Example for linear model
otu_stats = data.frame("Estimate" = numeric(0), "Std. Error"= numeric(0),"t value"= numeric(0),"Pr(>|t|)"= numeric(0))
for (otu in row.names(m.filtered)){
linear_fit = m.norm %>%
psmelt() %>%
filter(OTU==otu) %>%
lm(Abundance ~ Depth_m, .) %>%
summary()
otu_data = linear_fit$coefficients["Depth_m",]
otu_stats <- rbind(otu_stats, otu_data)
}
colnames(otu_stats)<- (c("Estimate", "Std. Error","t value","Pr(>|t|) (p-value)"))
row.names(otu_stats) <- row.names(m.filtered)
sorted = arrange(rownames_to_column(otu_stats),Estimate)%>% column_to_rownames(var="rowname")
lm.depth.otus = kable(sorted,caption="Table 1 Correlation data of Chloroflexi OTUs Abundance with Depth")
# Example for correlation graph
m.percent %>%
subset_taxa(Phylum==phylum_name_mothur) %>%
psmelt() %>%
ggplot() +
geom_point(aes(x=Depth_m, y=Abundance)) +
geom_smooth(method='lm', aes(x=Depth_m, y=Abundance)) +
facet_wrap(~OTU, scales="free_y") +
labs(title="Figure 7 Abundance of Chloroflexi OTUs across Depth") +
xlab("Depth (m)") +
ylab("Abundance (%)") +
theme(axis.text.x = element_text(angle = 90))
Linear model statistics were performed for the abundance of each OTU and ASV in relation to depth and oxygen concentration (Appendix A Table 1-4). The linear models were subsequently plotted (Figure 7-10). No significant correlations were found between any individual OTUs/ASVs abundance and depth or oxygen concentration (p > 0.05 for all). Some of the lack of significance could be attributed to the seemingly single outliers visible in many of the plots.
Although none of the correlations were significant, mothur and QIIME2 showed similar trends. For mothur ten of the 34 OTUs had negative correlation between abundance and depth (the rest positive), while for QIIME2 nine of the 47 ASVs had negative correlation between abundance and depth (the rest positive). This was while for abudance versus oxygen concentration, for mothur all OTUs had negative correlation, and for QIIME2 all but one ASVs had negative correlation.
As noted in the different sections, in general, major patterns found in a similar way in both mothur and QIIME2. Nevertheless, mothur and QIIME2 outputted in slightly different results as described in the different parts.
Sorted by correlation
| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| Otu0181 | -0.1713584 | 0.3794814 | -0.4515595 | 0.6704985 |
| Otu1579 | -0.0073322 | 0.0162544 | -0.4510917 | 0.6708136 |
| Otu1149 | -0.0035352 | 0.0082586 | -0.4280621 | 0.6864177 |
| Otu4286 | -0.0035352 | 0.0082586 | -0.4280621 | 0.6864177 |
| Otu1064 | -0.0027496 | 0.0165362 | -0.1662767 | 0.8744539 |
| Otu2632 | -0.0023568 | 0.0055057 | -0.4280621 | 0.6864177 |
| Otu4287 | -0.0011784 | 0.0027529 | -0.4280621 | 0.6864177 |
| Otu2381 | -0.0005237 | 0.0056008 | -0.0935100 | 0.9291298 |
| Otu2592 | -0.0005237 | 0.0056008 | -0.0935100 | 0.9291298 |
| Otu2591 | -0.0002619 | 0.0028004 | -0.0935100 | 0.9291298 |
| Otu1577 | 0.0001637 | 0.0036177 | 0.0452401 | 0.9656672 |
| Otu3712 | 0.0008511 | 0.0055928 | 0.1521723 | 0.8850009 |
| Otu3607 | 0.0034043 | 0.0023533 | 1.4465667 | 0.2076595 |
| Otu2790 | 0.0034043 | 0.0023533 | 1.4465667 | 0.2076595 |
| Otu3623 | 0.0036007 | 0.0053694 | 0.6705821 | 0.5322101 |
| Otu4340 | 0.0068085 | 0.0047067 | 1.4465667 | 0.2076595 |
| Otu2789 | 0.0068085 | 0.0047067 | 1.4465667 | 0.2076595 |
| Otu1558 | 0.0070049 | 0.0049223 | 1.4231039 | 0.2139907 |
| Otu1863 | 0.0079214 | 0.0046360 | 1.7086714 | 0.1482107 |
| Otu3589 | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Otu1419 | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Otu2497 | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Otu1147 | 0.0146645 | 0.0110438 | 1.3278449 | 0.2416173 |
| Otu1983 | 0.0158101 | 0.0123144 | 1.2838745 | 0.2554599 |
| Otu1246 | 0.0158429 | 0.0092720 | 1.7086714 | 0.1482107 |
| Otu1851 | 0.0170213 | 0.0117667 | 1.4465667 | 0.2076595 |
| Otu0662 | 0.0340426 | 0.0235333 | 1.4465667 | 0.2076595 |
| Otu0551 | 0.0365957 | 0.0465283 | 0.7865264 | 0.4671821 |
| Otu1028 | 0.0374468 | 0.0258867 | 1.4465667 | 0.2076595 |
| Otu0607 | 0.0389853 | 0.0438394 | 0.8892756 | 0.4145865 |
| Otu0799 | 0.0477578 | 0.0280660 | 1.7016226 | 0.1495636 |
| Otu0217 | 0.1946645 | 0.2102439 | 0.9258985 | 0.3969899 |
| Otu0215 | 0.4527660 | 0.3129935 | 1.4465667 | 0.2076595 |
| Otu0195 | 0.5344681 | 0.3694735 | 1.4465667 | 0.2076595 |
| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| Asv1886 | -0.3397709 | 0.2301978 | -1.4759954 | 0.1999714 |
| Asv800 | -0.1327332 | 0.3683890 | -0.3603073 | 0.7333378 |
| Asv1266 | -0.0329951 | 0.0770801 | -0.4280621 | 0.6864177 |
| Asv1289 | -0.0164975 | 0.0385401 | -0.4280621 | 0.6864177 |
| Asv1979 | -0.0057610 | 0.0616089 | -0.0935100 | 0.9291298 |
| Asv1144 | -0.0039280 | 0.1354082 | -0.0290085 | 0.9779801 |
| Asv341 | -0.0035352 | 0.0082586 | -0.4280621 | 0.6864177 |
| Asv1862 | -0.0034043 | 0.0364052 | -0.0935100 | 0.9291298 |
| Asv1260 | -0.0007856 | 0.0084012 | -0.0935100 | 0.9291298 |
| Asv2081 | 0.0011129 | 0.0027583 | 0.4034830 | 0.7032691 |
| Asv2034 | 0.0038298 | 0.0251674 | 0.1521723 | 0.8850009 |
| Asv1142 | 0.0057610 | 0.0801057 | 0.0719180 | 0.9454553 |
| Asv1046 | 0.0111293 | 0.0275831 | 0.4034830 | 0.7032691 |
| Asv2247 | 0.0126023 | 0.0187931 | 0.6705821 | 0.5322101 |
| Asv400 | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Asv496 | 0.0136170 | 0.0094133 | 1.4465667 | 0.2076595 |
| Asv2063 | 0.0189198 | 0.0468912 | 0.4034830 | 0.7032691 |
| Asv134 | 0.0234043 | 0.0349014 | 0.6705821 | 0.5322101 |
| Asv1473 | 0.0238298 | 0.0164733 | 1.4465667 | 0.2076595 |
| Asv1794 | 0.0238298 | 0.0164733 | 1.4465667 | 0.2076595 |
| Asv1234 | 0.0272340 | 0.0188267 | 1.4465667 | 0.2076595 |
| Asv477 | 0.0288052 | 0.0429556 | 0.6705821 | 0.5322101 |
| Asv590 | 0.0306383 | 0.0211800 | 1.4465667 | 0.2076595 |
| Asv1003 | 0.0306383 | 0.0211800 | 1.4465667 | 0.2076595 |
| Asv1282 | 0.0340426 | 0.0235333 | 1.4465667 | 0.2076595 |
| Asv490 | 0.0396072 | 0.0859823 | 0.4606434 | 0.6643958 |
| Asv1664 | 0.0414075 | 0.0617486 | 0.6705821 | 0.5322101 |
| Asv1939 | 0.0418331 | 0.0911012 | 0.4591934 | 0.6653680 |
| Asv1163 | 0.0476596 | 0.0329467 | 1.4465667 | 0.2076595 |
| Asv473 | 0.0522095 | 0.0778570 | 0.6705821 | 0.5322101 |
| Asv2315 | 0.0578723 | 0.0400067 | 1.4465667 | 0.2076595 |
| Asv1693 | 0.0583633 | 0.0676342 | 0.8629259 | 0.4276204 |
| Asv555 | 0.0748936 | 0.0517734 | 1.4465667 | 0.2076595 |
| Asv1943 | 0.0792144 | 0.1181278 | 0.6705821 | 0.5322101 |
| Asv428 | 0.0955810 | 0.1216822 | 0.7854968 | 0.4677332 |
| Asv114 | 0.1054664 | 0.1601592 | 0.6585100 | 0.5393201 |
| Asv2324 | 0.1089362 | 0.0753067 | 1.4465667 | 0.2076595 |
| Asv1423 | 0.1123404 | 0.0776600 | 1.4465667 | 0.2076595 |
| Asv1505 | 0.1123404 | 0.0776600 | 1.4465667 | 0.2076595 |
| Asv271 | 0.1361702 | 0.0941334 | 1.4465667 | 0.2076595 |
| Asv208 | 0.1468412 | 0.1522869 | 0.9642409 | 0.3792105 |
| Asv1095 | 0.1634043 | 0.1129601 | 1.4465667 | 0.2076595 |
| Asv161 | 0.1668085 | 0.1153134 | 1.4465667 | 0.2076595 |
| Asv1108 | 0.1669722 | 0.1917355 | 0.8708462 | 0.4236697 |
| Asv408 | 0.1859247 | 0.2109964 | 0.8811750 | 0.4185601 |
| Asv1071 | 0.3438298 | 0.2376868 | 1.4465667 | 0.2076595 |
| Asv1749 | 0.5208511 | 0.3600602 | 1.4465667 | 0.2076595 |
| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| Otu0195 | -0.1897296 | 0.3302310 | -0.5745358 | 0.5904867 |
| Otu0217 | -0.1736086 | 0.1582994 | -1.0967102 | 0.3227568 |
| Otu0215 | -0.1607263 | 0.2797499 | -0.5745358 | 0.5904867 |
| Otu0181 | -0.0520091 | 0.2990620 | -0.1739074 | 0.8687601 |
| Otu0607 | -0.0317385 | 0.0336870 | -0.9421584 | 0.3893700 |
| Otu0551 | -0.0269047 | 0.0362727 | -0.7417334 | 0.4915977 |
| Otu0799 | -0.0200649 | 0.0258114 | -0.7773675 | 0.4721013 |
| Otu1028 | -0.0132932 | 0.0231372 | -0.5745358 | 0.5904867 |
| Otu0662 | -0.0120847 | 0.0210338 | -0.5745358 | 0.5904867 |
| Otu1983 | -0.0084593 | 0.0103315 | -0.8187858 | 0.4501563 |
| Otu1147 | -0.0084593 | 0.0092049 | -0.9189965 | 0.4002601 |
| Otu1246 | -0.0072508 | 0.0084400 | -0.8590967 | 0.4295406 |
| Otu1851 | -0.0060423 | 0.0105169 | -0.5745358 | 0.5904867 |
| Otu1419 | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Otu2497 | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Otu3589 | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Otu1558 | -0.0036254 | 0.0042200 | -0.8590967 | 0.4295406 |
| Otu1863 | -0.0036254 | 0.0042200 | -0.8590967 | 0.4295406 |
| Otu2789 | -0.0024169 | 0.0042068 | -0.5745358 | 0.5904867 |
| Otu4340 | -0.0024169 | 0.0042068 | -0.5745358 | 0.5904867 |
| Otu3623 | -0.0024169 | 0.0042068 | -0.5745358 | 0.5904867 |
| Otu1064 | -0.0020728 | 0.0128145 | -0.1617557 | 0.8778315 |
| Otu1579 | -0.0012945 | 0.0128349 | -0.1008584 | 0.9235824 |
| Otu3712 | -0.0012919 | 0.0043048 | -0.3001126 | 0.7761680 |
| Otu2790 | -0.0012085 | 0.0021034 | -0.5745358 | 0.5904867 |
| Otu3607 | -0.0012085 | 0.0021034 | -0.5745358 | 0.5904867 |
| Otu1577 | -0.0009643 | 0.0027703 | -0.3480925 | 0.7419469 |
| Otu2592 | -0.0006367 | 0.0043341 | -0.1469078 | 0.8889445 |
| Otu2381 | -0.0006367 | 0.0043341 | -0.1469078 | 0.8889445 |
| Otu1149 | -0.0004881 | 0.0065115 | -0.0749568 | 0.9431557 |
| Otu4286 | -0.0004881 | 0.0065115 | -0.0749568 | 0.9431557 |
| Otu2632 | -0.0003254 | 0.0043410 | -0.0749568 | 0.9431557 |
| Otu2591 | -0.0003184 | 0.0021670 | -0.1469078 | 0.8889445 |
| Otu4287 | -0.0001627 | 0.0021705 | -0.0749568 | 0.9431557 |
| Estimate | Std. Error | t value | Pr(>|t|) (p-value) | |
|---|---|---|---|---|
| Asv1749 | -0.1848957 | 0.3218175 | -0.5745358 | 0.5904867 |
| Asv408 | -0.1764060 | 0.1570152 | -1.1234959 | 0.3122557 |
| Asv1108 | -0.1646951 | 0.1413959 | -1.1647802 | 0.2966535 |
| Asv208 | -0.1439410 | 0.1112113 | -1.2943021 | 0.2521126 |
| Asv1071 | -0.1220553 | 0.2124416 | -0.5745358 | 0.5904867 |
| Asv428 | -0.1061416 | 0.0879361 | -1.2070308 | 0.2814027 |
| Asv114 | -0.0969221 | 0.1218861 | -0.7951862 | 0.4625657 |
| Asv800 | -0.0875482 | 0.2864534 | -0.3056281 | 0.7722028 |
| Asv161 | -0.0592150 | 0.1030657 | -0.5745358 | 0.5904867 |
| Asv1095 | -0.0580065 | 0.1009624 | -0.5745358 | 0.5904867 |
| Asv1943 | -0.0531726 | 0.0925488 | -0.5745358 | 0.5904867 |
| Asv271 | -0.0483387 | 0.0841353 | -0.5745358 | 0.5904867 |
| Asv1939 | -0.0476310 | 0.0688397 | -0.6919125 | 0.5198017 |
| Asv490 | -0.0452141 | 0.0649448 | -0.6961928 | 0.5173357 |
| Asv1693 | -0.0447133 | 0.0524914 | -0.8518223 | 0.4332067 |
| Asv1505 | -0.0398795 | 0.0694116 | -0.5745358 | 0.5904867 |
| Asv1423 | -0.0398795 | 0.0694116 | -0.5745358 | 0.5904867 |
| Asv2324 | -0.0386710 | 0.0673082 | -0.5745358 | 0.5904867 |
| Asv473 | -0.0350456 | 0.0609981 | -0.5745358 | 0.5904867 |
| Asv1664 | -0.0277948 | 0.0483778 | -0.5745358 | 0.5904867 |
| Asv555 | -0.0265863 | 0.0462744 | -0.5745358 | 0.5904867 |
| Asv1144 | -0.0252671 | 0.1043155 | -0.2422180 | 0.8182327 |
| Asv2063 | -0.0205440 | 0.0357575 | -0.5745358 | 0.5904867 |
| Asv2315 | -0.0205440 | 0.0357575 | -0.5745358 | 0.5904867 |
| Asv477 | -0.0193355 | 0.0336541 | -0.5745358 | 0.5904867 |
| Asv1163 | -0.0169186 | 0.0294474 | -0.5745358 | 0.5904867 |
| Asv134 | -0.0157101 | 0.0273440 | -0.5745358 | 0.5904867 |
| Asv1282 | -0.0120847 | 0.0210338 | -0.5745358 | 0.5904867 |
| Asv1046 | -0.0120847 | 0.0210338 | -0.5745358 | 0.5904867 |
| Asv1142 | -0.0112835 | 0.0618942 | -0.1823031 | 0.8625058 |
| Asv590 | -0.0108762 | 0.0189304 | -0.5745358 | 0.5904867 |
| Asv1003 | -0.0108762 | 0.0189304 | -0.5745358 | 0.5904867 |
| Asv1234 | -0.0096677 | 0.0168271 | -0.5745358 | 0.5904867 |
| Asv1794 | -0.0084593 | 0.0147237 | -0.5745358 | 0.5904867 |
| Asv1473 | -0.0084593 | 0.0147237 | -0.5745358 | 0.5904867 |
| Asv2247 | -0.0084593 | 0.0147237 | -0.5745358 | 0.5904867 |
| Asv1979 | -0.0070038 | 0.0476747 | -0.1469078 | 0.8889445 |
| Asv2034 | -0.0058137 | 0.0193716 | -0.3001126 | 0.7761680 |
| Asv496 | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Asv400 | -0.0048339 | 0.0084135 | -0.5745358 | 0.5904867 |
| Asv1266 | -0.0045554 | 0.0607736 | -0.0749568 | 0.9431557 |
| Asv1862 | -0.0041386 | 0.0281714 | -0.1469078 | 0.8889445 |
| Asv1289 | -0.0022777 | 0.0303868 | -0.0749568 | 0.9431557 |
| Asv2081 | -0.0012085 | 0.0021034 | -0.5745358 | 0.5904867 |
| Asv1260 | -0.0009551 | 0.0065011 | -0.1469078 | 0.8889445 |
| Asv341 | -0.0004881 | 0.0065115 | -0.0749568 | 0.9431557 |
| Asv1886 | 0.1614351 | 0.2011515 | 0.8025547 | 0.4586642 |